Improvement in electrical synchrony during bi-ventricular vs. left ventricular pacing with dynamic atrioventricular delays may be predicted by conduction times
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Abbott Introduction Automatic adjustment of atrioventricular delay (AVD) with SyncAV has been shown to improve electrical synchrony with either biventricular (BiV) or left ventricular (LV) only pacing. Selecting the optimal pacing mode may be guided by measuring conduction delays between the right atrium (RA), right ventricle (RV), and LV. Purpose Evaluate correlations between inter-chamber conduction delays and the QRS duration (QRSd) reduction achieved by BiV or LV-only pacing with SyncAV. Methods CRT implant patients (LBBB, QRSd ≥ 150 ms) were prospectively enrolled. Blinded QRSd was measured from 12-lead ECG during BiV and LV-only pacing, with SyncAV enabled and optimized to minimize QRSd. Conduction delays were measured by the device from unipolar electrograms during pacing and sensing (e.g. RAp-RVs). Correlations between each delay and which mode had the narrower QRSd was assessed by binomial regression. Results In total, 68 patients were evaluated (66.1 yr, 67.1% male, 32.5% ischemic, 26.3% EF, 165.1 ms intrinsic QRSd). BiV+SyncAV and LV+SyncAV reduced QRSd by 23.8% and 21.3% (P<0.001) vs. intrinsic conduction. Of all delays, RVs-LVs and LVp-RVs were significantly correlated with patient probability of BiV+SyncAV yielding a narrower QRSd than LV+SyncAV. BiV+SyncAV was favored in 70.6% (48/68) of all patients, but was favored in 92.3% (24/26) of patients with LVp-RVs < median (143 ms) (P=0.030 vs all). Conclusion When combined with SyncAV, BiV yielded a narrower QRSd than LV-only in the majority of patients, but was favored in a greater proportion who exhibit LVp-RVs delays below the median. Such conduction delay cut-offs can be used to facilitate pacing mode selection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".